Abstract
Motivation: Correlated events of gains and losses enable inference of co-evolution relations. The reconstruction of the co-evolutionary interactions network in prokaryotic species may elucidate functional associations among genes.Results: We developed a novel probabilistic methodology for the detection of co-evolutionary interactions between pairs of genes. Using this method we inferred the co-evolutionary network among 4593 Clusters of Orthologous Genes (COGs). The number of co-evolutionary interactions substantially differed among COGs. Over 40% were found to co-evolve with at least one partner. We partitioned the network of co-evolutionary relations into clusters and uncovered multiple modular assemblies of genes with clearly defined functions. Finally, we measured the extent to which co-evolutionary relations coincide with other cellular relations such as genomic proximity, gene fusion propensity, co-expression, protein–protein interactions and metabolic connections. Our results show that co-evolutionary relations only partially overlap with these other types of networks. Our results suggest that the inferred co-evolutionary network in prokaryotes is highly informative towards revealing functional relations among genes, often showing signals that cannot be extracted from other network types.Availability and implementation: Available under GPL license as open source.Contact: talp@post.tau.ac.il.Supplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Prokaryotic genomes are highly variable in their size (Koonin and Wolf, 2008; Mira et al, 2002)
Comparative genomic analyses revealed that variability in gene content among genomes is a major factor contributing to this size variability (Konstantinidis and Tiedje, 2004; Pal et al, 2005)
Gene content across genomes is compactly represented by phyletic patterns, in which the presence or absence of each COG (Clusters of Orthologous Genes) in each genome is represented as a 0/1 binary character
Summary
Prokaryotic genomes are highly variable in their size (Koonin and Wolf, 2008; Mira et al, 2002). Comparative genomic analyses revealed that variability in gene content among genomes is a major factor contributing to this size variability (Konstantinidis and Tiedje, 2004; Pal et al, 2005). This substantial variability is mainly the result of gene acquisition via Horizontal Gene Transfer (HGT) (Gogarten and Townsend, 2005) and gene loss, e.g. as a result of reductive evolution (Moran, 2003). This may reflect mutual dependency constraints, e.g. when these genes correspond to proteins that are part of a complex. Detecting such co-evolutionary interactions is important for understanding genome evolution as a coordinated process rather than as a collection of single evolutionary descriptions of each gene
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